Palmprint Recognition Based on Multi-Scale Gabor Orientation Weber Local Descriptors

Mengwen Li; Huaiyu Liu; Xiangjun Gao; Qianqian Meng

doi:10.3788/LOP202158.1610018

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 16 >Page 1610018 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 16, 1610018 (2021)

Palmprint Recognition Based on Multi-Scale Gabor Orientation Weber Local Descriptors

Mengwen Li, Huaiyu Liu^*, Xiangjun Gao, and Qianqian Meng

Author Affiliations

College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui 235000, China

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DOI: 10.3788/LOP202158.1610018 Cite this Article Set citation alerts

Mengwen Li, Huaiyu Liu, Xiangjun Gao, Qianqian Meng. Palmprint Recognition Based on Multi-Scale Gabor Orientation Weber Local Descriptors[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1610018 Copy Citation Text

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Fig. 1. Computation of differential excitation and gradient orientation. (a) Differential excitation; (b) gradient orientation

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Fig. 2. Illustration of construction of 2D WLD features

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Fig. 3. Orientation values of different pixels

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Fig. 4. Gabor filter with 5 scales and 6 orientations

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Fig. 5. Energy maps and orientation maps of a plamprint image. (a) Energy maps; (b) orientation maps

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Fig. 6. Differential excitation values of different pixels

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Fig. 7. Differential excitation of energy maps at different scales. (a) v=0;(b) v=1;(c) v=2;(d) v=3;(e) v=4

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Fig. 8. Process of MGOWLD feature extraction

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Fig. 9. Examples of palmprint images collected from different palmprint databases. (a) PolyU;(b) Blue;(c) Green;(d) Red;(e) NIR;(f) CASIA

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Fig. 10. ROIs of palmprint images

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Fig. 11. IR of different palmprint recognition methods. (a) PolyU;(b) Blue;(c) Green;(d) Red;(e) NIR;(f) CASIA

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Fig. 12. Distributions of matching scores on different palmprint databases. (a) Blue;(b) Green; (c) Red;(d) NIR;(e) PolyU;(f) CASIA

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Fig. 13. ROC curves of different methods. (a) Blue;(b) Green;(c) Red;(d) NIR;(e) PolyU;(f) CASIA

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Fig. 14. Palmprint images with different levels of Gaussian noise. (a) Variance is 10;(b) variance is 20; (c) variance is 30;(d) variance is 60;(e) variance is 80;(f) variance is 100

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Fig. 15. Energy maps of noisy palmprint image. (a) NIR palmprint images;(b) CASIA palmprint images

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Input:palmprint image IOutput:feature vector F

1. Utilize Eq.(13)--Eq.(16) to generate Gabor filters G_u_,_v with V scales and U orientations2. Image I is filtered by G_u_,_v,and utilize Eq.(18)--Eq.(19) to generate energy maps E_v and orientation maps O_v3. Following operations are performed on energy maps and orientation maps of each scale: 3.1 E_v and O_v are divided into N non-overlapping regions,and each region is recorded as

E_{v}^{(n)}

and

O_{v}^{(n)}

,respectively 3.2 Utilize Eq.(21)--Eq.(24) to calculate differential excitation ξ

_{m}^{(n)}

for each

E_{v}^{(n)}

3.3 Obtain statistics of feature histogram

\{H_{MGOWLD}^{(n)} (ξ_{m}^{(n)}, O_{v}^{(n)})\}

for each differential excitation ξ

_{m}^{(n)}

and orientation

O_{v}^{(n)}

3.4 Each column of

\{H_{MGOWLD}^{(n)} (ξ_{m}^{(n)}, O_{v}^{(n)})\}

is connected to form one-dimensional feature vector f_n 3.5 Concatenate the feature vector f_n of each block to form vector F_v=

[f_{1}, f_{2}, \dots, f_{N}]

as the feature of scale v4. Feature vectors F_v of all scales are connected to form the feature vector F=

[F_{1}, F_{2}, \dots, F_{V}]

Table 1. Steps of MGOWLD feature extraction

	MGOWLD	LBP	ALDC_A	ALDC_M	HOG	LDDBP	DOC	HOC	LWLD	AWASTP
Blue	0.0272	3.1111	0.5661	0.5556	0.8036	0.2444	2.6054	1.8762	0.1778	4.0057
Green	0.0667	8.4188	0.6000	0.5778	1.1333	0.3778	2.6360	2.0667	0.1803	5.3333
Red	0.0444	3.6944	0.5890	0.5885	0.8895	0.2667	2.0295	1.6071	0.4667	5.1549
NIR	0.0461	4.6135	1.0415	1.0444	1.7260	0.3847	2.1487	1.8205	0.6629	5.1773
PolyU	0.1594	16.7197	1.6341	1.6939	3.5336	1.0761	4.8219	4.1405	0.8504	12.1125
CASIA	1.4887	8.7061	8.9968	8.5319	4.8199	2.6838	8.1119	10.6451	5.7985	5.6311

Table 2. EER of different palmprint recognition methods unit: %

Variance	0	10	20	30	60	80	100
Blue	100	100	99.97	100	99.87	99.80	99.47
Green	99.97	99.93	99.90	99.90	99.77	99.43	99.20
Red	100	99.77	99.67	99.03	97.90	96.30	93.37
NIR	100	99.73	99.20	98.30	90.10	82.30	73.83
PolyU	99.88	99.86	99.86	99.88	99.88	99.86	99.84
CASIA	98.32	96.50	95.40	93.92	89.84	86.73	83.95

Table 3. IR of MGOWLD under different degrees of noise pollution unit: %

Variance	0	10	20	30	60	80	100
Blue	0.0272	0.0457	0.0654	0.0849	0.2000	0.2222	0.3193
Green	0.0667	0.0889	0.1436	0.1704	0.2310	0.2889	0.4498
Red	0.0444	0.2172	0.2667	0.4080	0.8581	1.1568	1.5556
NIR	0.0461	0.2495	0.3778	0.7111	2.0498	3.1173	4.3846
PolyU	0.1594	0.1993	0.2050	0.2192	0.2391	0.3310	0.3384
CASIA	1.4887	2.5133	3.0454	3.2362	4.5227	5.5617	6.0003

Table 4. EER of MGOWLD under different degrees of noise pollution unit: %

Database	PCANet		PalmNet		MGOWLD
Database	IR	EER	IR	EER	IR	EER
Blue	100	0.0014	86.30	13.3932	100	0.0004
Green	100	0.0032	93.70	6.3000	100	0.0078
Red	100	0.0910	86.40	11.3795	100	0.1000
NIR	100	0.0215	87.20	11.6880	100	0.0083
PolyU	99.80	0.2000	90.20	9.9066	99.80	0.1940
CASIA	95.70	2.8340	73.00	23.1894	98.70	1.4145

Table 5. IR and EER of three palmprint recognition methods on different databases unit: %

Method	Feature extract	Feature matching	Total time
MGOWLD	0.560	0.052	0.612
PCANet	0.430	6.580	7.010
PalmNet	0.750	6.150	6.900

Table 6. Time cost of different palmprint recognition methods unit: s

Mengwen Li, Huaiyu Liu, Xiangjun Gao, Qianqian Meng. Palmprint Recognition Based on Multi-Scale Gabor Orientation Weber Local Descriptors[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1610018

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